Lung adenocarcinoma (LUAD) has a high mortality rate. N6-methyl-adenosine (m6A)-related long noncoding RNA (lncRNA) is associated with tumor prognosis. Our objective was to construct an m6A-related lncRNA prognostic model and screen potential drugs for the treatment of LUAD. The LUAD sequencing data were randomly divided into Train and Test cohorts. In the Train group, the LASSO Cox regression was used to construct the m6A-related lncRNA prognostic model. The LUAD tumor immune dysfunction and exclusion model was used to evaluate immunotherapy efficacy in LUAD. The 'pRRophetic' package was utilized to screen potential drugs for the treatment of LUAD. Eleven m6A-related lncRNAs were identified by LASSO Cox regression and were used to construct the risk model to calculate sample risk scores. Patients were divided into high-and low-risk groups based on their median risk scores. The LUAD data of The Cancer Genome Atlas database showed that the overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group in both cohorts. Multivariate Cox regression analysis showed that this risk model could serve as an independent prognostic factor of LUAD, and receiver operating characteristic curves suggested that m6A-related lncRNA prognostic signature has a good ability in predicting OS. Finally, nine potential drugs for LUAD treatment were screened based on this prognostic model. The prognostic model constructed based on the m6A-related lncRNAs facilitated prognosis prediction in LUAD patients. The screened therapeutic agents have potential application values and provide a reference for the clinical treatment of LUAD.
Dementia is a well-known syndrome and Alzheimer's disease (AD) is the main cause of dementia. Lipids play a key role in the pathogenesis of AD, however, the prediction value of serum lipidomics on AD remains unclear. This study aims to construct a lipid score system to predict the risk of progression from mild cognitive impairment (MCI) to AD. First, we used the least absolute shrinkage and selection operator (LASSO) Cox regression model to select the lipids that can signify the progression from MCI to AD based on 310 older adults with MCI.Then we constructed a lipid score based on 14 single lipids using Cox regression and estimated the association between the lipid score and progression from MCI to AD. The prevalence of AD in the low-, intermediate-and high-score groups was 42.3%, 59.8%, and 79.8%, respectively. The participants in the intermediateand high-score group had a 1.65-fold (95% CI 1.10 to 2.47) and 3.55-fold (95% CI 2.40 to 5.26) higher risk of AD, respectively, as compared to those with low lipid scores. The lipid score showed moderate prediction efficacy (c-statistics > 0.72).These results suggested that the score system based on serum lipidomics is useful for the prediction of progression from MCI to AD.
Background Adenocarcinoma has long been an independent histological class of lung cancer, which leads to high morbidity and mortality. We aimed to investigate the contribution of LINC02126 in lung adenocarcinoma. Methods RNA sequencing data and clinical information were downloaded. Diagnostic efficiency and survival analysis of LINC02126 were performed, followed by functional analysis of genes co-expressed with LINC02126 and differentially expressed genes (DEGs) in different LINC02126 expression groups. Tumor immune microenvironment (TIME) cell infiltration and correlation analysis of tumor mutation burden were performed in different LINC02126 expression groups. Results In lung adenocarcinoma, the expression level of LINC02126 was significantly decreased. Significant expression differences of LINC02126 were found in some clinical variables, including T staging, M staging, sex, stage, and EGFR mutation. LINC02126 had potential diagnostic and prognostic value for patients. In the low LINC02126 expression group, the infiltration degree of most immune cells was significantly lower than that in the high LINC02126 expression group. Tumor mutation burden level and frequency of somatic mutation in patients with low LINC02126 expression group were significantly higher than in patients with high LINC02126 expression group. Conclusions LINC02126 could be considered as a diagnostic, prognostic and immunotherapeutic target for lung adenocarcinoma.
Aims: The study aimed to explore the effect of metabolism on lung cancer. Background: The tumor microenvironment is largely influenced by metabolism, tightly involved in tumor progression. Objective: We try to investigate the effect of tumor metabolism terms on non-small cell lung cancer (NSCLC) prognosis, drug and immunotherapy sensitivity, as well as its underlying mechanisms. Methods: All the data was obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. R software was used to perform all statistical analyses and plots. Results: This study conducted 21 metabolism statuses in NSCLC to identify their underlying roles. We found that alpha-linolenic acid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, fatty acid degradation, linoleic acid metabolism, primary bile acid biosynthesis, and fatty acid metabolism were protective factors for NSCLC. Next, we constructed a prognosis model based on primary bile acid biosynthesis, glycerophospholipid, and sphingolipid metabolism. Results in the present study showed that our model could effectively predict patients' prognosis in both training and validation cohorts. A clinical correlation revealed that patients at high-risk exhibited more progressive clinical characteristics. Biological enrichment indicated that MYC targets, E2F targets, mTORC1 signaling, G2/M checkpoint, and epithelial-mesenchymal transition were activated in the high-risk group. Immune relation analysis showed that risk score positively correlated with Th2 cells, yet a negative correlation with CD56 bright NK, Th17, mast and CD8+ T cells. Moreover, our model was related to NSCLC patients' sensitivity to immunotherapy and chemotherapy. Ultimately, eight characteristic genes were identified to distinguish the patients' risk group in the real application. Conclusions: The model we developed is a useful tool to predict NSCLC patients' prognosis and is associated with the sensitivity of immunotherapy and chemotherapy. Meanwhile, our results can guide the following metabolism-related studies in NSCLC.
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